Fragility of asymptotic agreement under Bayesian learning

, (), , () and , ()
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,: Department of Economics, MIT
,: Department of Economics, MIT
,: Department of Economics, MIT
Authors registered in the RePEc Author Service: Muhamet Yildiz (), Daron Acemoglu and Victor Chernozhukov

Theoretical Economics, 2016, vol. 11, issue 1

Abstract: Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are fragile when individuals are uncertain about the signal distributions: given any such model, vanishingly small individual uncertainty about the signal distributions can lead to substantial (non-vanishing) differences in asymptotic beliefs. Under a uniform convergence assumption, we then characterize the conditions under which a small amount of uncertainty leads to significant asymptotic disagreement.

Keywords: Asymptotic disagreement; Bayesian learning; merging of opinions (search for similar items in EconPapers)
JEL-codes: C11 C72 D83 (search for similar items in EconPapers)
Date: 2016-01-30
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Citations: View citations in EconPapers (43)

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Related works:
Working Paper: Fragility of Asymptotic Agreement under Bayesian Learning (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:the:publsh:436

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